Data Engineer

• Lead the design, development, and continuous modernization of all CMS sales training curriculum across onboarding, advanced development, and leadership programs
• Architect and deploy scalable digital learning ecosystems, including LMS platforms, virtual delivery models, and AI-driven learning tools
• Integrate artificial intelligence to personalize learning pathways, enhance coaching effectiveness, and drive predictive performance insights
• Develop data platforms and dashboards that connect training initiatives to field execution, adoption, and business performance
• Establish standardized curriculum frameworks grounded in adult learning principles, behavioral change theory, and competency-based development
• Partner with field leadership to identify capability gaps and develop targeted programs to elevate clinical expertise and business acumen
• Lead transformation of existing training content into modern, interactive, and digitally enabled learning experiences
• Drive cross-functional collaboration with Marketing, Sales Operations, and Medical Affairs to ensure alignment with product strategy, clinical evidence, and market dynamics
• Continuously evaluate emerging technologies, AI capabilities, and digital learning trends to keep ZOLL at the forefront of medical device training innovation
• Support and enhance national and regional sales meeting content through innovative curriculum design and delivery strategies
• Elevate clinical acumen across the organization by developing advanced training focused on cardiac physiology, therapy application, and patient pathways
• Strengthen business acumen by building programs focused on territory strategy, account prioritization, and value-based selling
• Enable managers to effectively coach to both clinical and commercial competencies
• Create differentiated learning journeys for new, developing, and tenured representatives to continuously advance performance.

Curriculum Development Manager, Data Platforms

• Lead the design and evolution of the data ecosystem supporting advanced data forensics, fraud detection, investigative analytics, and threat intelligence
• Establish the architecture, governance, and technical foundations necessary to deliver scalable analytics, machine learning, and investigative intelligence capabilities
• Design and maintain enterprise-scale data architectures supporting forensic analytics and security operations
• Develop target-state architectures that support both operational reporting and advanced analytical workloads
• Architect and optimize cloud-based analytics platforms utilizing Azure and Databricks technologies
• Implement enterprise data governance standards and controls
• Build and maintain architectures that support machine learning, artificial intelligence, and advanced analytics workloads.

Advanced Specialist, Data Engineering

• Partner with business stakeholders, Product Owners, SMEs, analysts, and development teams to understand business processes, data requirements, source systems, data mappings, and data consumption needs.
• Translate business requirements, processes, and rules into logical and physical data models, source-to-target mappings, and data integration specifications.
• Define and implement test data strategies that support functional, regression, integration, performance, automation, and modernization testing needs.
• Design and govern reusable test data patterns, test data provisioning approaches, masking strategies, synthetic data solutions, and environment refresh processes.
• Demonstrate strong understanding of data modeling principles, including entities, attributes, relationships, schemas, primary and foreign keys, indexes, constraints, and overall database design.
• Read, interpret, and analyze Entity Relationship Diagrams, database schemas, data dictionaries, and end-to-end data flow diagrams to identify data dependencies and business impact.
• Perform data discovery, analysis, and data mining across multiple databases, tables, files, and source systems using SQL, Easytrieve, and other data query and extraction tools.
• Validate data quality, integrity, consistency, and accuracy by analyzing data relationships, transformation logic, reconciliation results, and business rules across systems.
• Develop an understanding of data movement and integration patterns, including real-time and batch processing, event-driven architectures, APIs, messaging queues, data pipelines, and file-based interfaces.
• Collaborate with architecture, development, testing, and operational teams to support data analysis, problem resolution, impact assessments, test automation enablement, and system modernization initiatives.
• Ensure test data solutions comply with data privacy, data protection, retention, classification, and regulatory requirements for sensitive customer and financial data.
• Document data models, test data design patterns, source-to-target mappings, provisioning processes, standards, and best practices for repeatable enterprise use.

Data Architect

• Lead a team of 2-3 engineers
• Working with fellow Nava engineers to design, review, and build well-crafted software
• Working in an agile manner to efficiently ship new features that meet user needs
• Design and develop scalable data ingestion and processing pipelines
• Implement large-scale data ecosystems within cloud-based platforms that include data management and data governance of structured and unstructured data
• Implement data validation and quality checks to ensure accuracy and consistency
• Work with cross-functional project teams to gather business requirements and translate to detailed technical specifications
• Work with Government partners to assist and develop data engineering applications and pipelines that will enable data services and processing capabilities
• Participate in software design and code reviews
• Maintain security and privacy standards in all aspects of the data pipeline
• Taking part in hiring activities (e.g., submitting referrals, conducting interviews, and attending interview debriefs), as needed

Senior Software Engineer, Data

• Design and implement the canonical data model for Vultr’s control plane ERP transformation: product catalog, chart of accounts, financial transactions, cost centers
• Build and maintain the internal APIs and event streams that Finance, Analytics, and downstream systems consume
• Refactor existing administer code toward clean domain boundaries (no more conflated orders/ledger/report tables)
• Implement data upgrade/migration paths from legacy schemas without breaking production
• Write the integration layer between administer and Vultr's existing billing, provisioning, and reporting systems
• Establish data validation and integrity checks across the financial data layer
• Collaborate with the Product Manager to translate finance domain requirements into durable schema designs
• Document architectural decisions and data model contracts for downstream consumers

Senior Software Engineer, ERP, Data & Domain

• Own the path from raw transactional and event data to trustworthy, well-modeled datasets powering BetMGM's analytics, ML, and operational systems.
• Design, build, and operate batch, micro-batch, and streaming pipelines feeding Snowflake — Prefect-orchestrated flows on ECS Fargate, dbt for transformation, Snowpipe Streaming and Kafka for event ingestion.
• Own the full dbt lifecycle (sources → staging → intermediate → marts) with model contracts, freshness SLAs, automated tests, and version-controlled documentation.
• Stand up Snowflake objects (warehouses, RBAC, resource monitors, Dynamic Tables, Iceberg tables) through Terraform — no ClickOps in production.
• Build AWS-native infrastructure for data workloads — S3, ECS Fargate, Lambda, EMR Serverless, Glue Catalog, IAM, Secrets Manager, VPC endpoints — entirely in Terraform.
• Maintain CI/CD pipelines (GitLab CI or GitHub Actions) that gate every change with linting, dbt build, unit tests, contract checks, and AI-assisted code review.
• Tune warehouse sizing, clustering, and query patterns for cost and latency; instrument credit usage via ACCOUNT_USAGE; right-size before scaling up.
• Design RBAC, masking policies, and row-access policies that satisfy a regulated operator without becoming an access bottleneck.
• Own freshness SLAs and data contracts for the gold layer; configure Monte Carlo coverage for volume, freshness, schema, and distribution; triage incidents end-to-end.
• Collaborate with analytics engineers, data scientists, and ML platform engineers on shared standards (naming, testing, observability, lineage, cost attribution).

Senior Data Engineer

• Lead strategy, prioritization, and roadmap alignment across multiple internal platform capability areas
• Own the strategy, roadmap, and evolution of internal data platform capabilities, including data ingestion, cross-cutting data services, the Enterprise Data Layer (EDL), data quality, metadata management, governance capabilities, and source onboarding
• Partner with Vizient Intelligence engineering, AI Delivery, architecture, security, and product leadership to define platform investment priorities
• Establish decision frameworks that sequence platform capabilities, address technical debt, improve platform reuse, enhance internal developer productivity, and support AI-assisted and agentic development workflows
• Align platform roadmaps with measurable outcomes, including reduced delivery friction, increased platform adoption, stronger governance, faster product delivery, and improved AI-first delivery maturity
• Drive platform capabilities that improve the reliability, accessibility, and reuse of enterprise data across Vizient Intelligence products
• Collaborate closely with data engineering teams to define platform capabilities that support scalable, high-quality, and governed data solutions
• Facilitate executive stakeholder alignment on platform direction, funding, dependencies, tradeoffs, adoption expectations, and AI-first operating model changes
• Represent platform product priorities in leadership forums by translating technical platform strategies into business-relevant decisions and resolving complex cross-functional priorities
• Guide and develop a small team of platform product professionals through coaching, mentorship, and professional development
• Establish lightweight platform product management standards, including discovery practices, roadmap narratives, success measures, prompt and context patterns, release planning, adoption planning, and responsible AI tool usage
• Build strong partnerships across engineering, AI Delivery, architecture, security, design, and internal product teams to enable consistent platform adoption and delivery outcomes

Lead Director, Platform Product Management – Data Platform & Operations

• The intent of this position is to provide our internal employees, 2026 Travelers Summer Interns and Summer Students the ability to apply to our 2027 Full-time or Intern opportunities.
• Innovative thinking, creative problem-solving, and a determination to challenge the status-quo, these are some of the defining qualities for participants of our Engineering Development Program (EDP).
• The EDP is a one-year program where you will be placed in one of the four designated Engineering tracks: Software, Data, Cybersecurity or Infrastructure & Cloud.
• Each track is designed to grow and develop your technical and self-leadership skills.
• Within your assigned track, you will receive a combination of hands-on opportunities, an immersive learning journey, and a mentoring program to prepare you for a dynamic career.
• EDP Participants in this track will partner closely with the business to refine features, plan user stories, and execute on the analysis, design, development, and testing of those user stories to deliver business capabilities to market.
• Additional program components include mentoring, networking, and career guidance.
• Please note that occasional travel for business meetings and events is an expectation of program participants.
• Perform other duties as assigned.

Engineering Development Program – Data Engineering

• Design, build and maintain scalable data pipelines and orchestration workflows using Airflow and modern data stack technologies
• Develop and optimize data ingestion processes from diverse sources to support analytics and business applications
• Develop and maintain analytics engineering workflows using dbt, SQL, and Python to transform raw data into analytics-ready models
• Partner with analytics, BI and IT to perform code reviews, establishing best practices, and mentoring junior engineers on data DevOps principles and tooling
• Collaborate with analytics and BI teams to understand requirements and deliver robust data solutions
• Monitor pipeline performance, data quality metrics, and system health while implementing proactive improvements
• Manage data infrastructure as code, CI/CD pipelines, and deployment automation for data applications within established team standards
• Document technical specifications, architectural decisions, to ensure knowledge sharing and maintainability

Staff Data Engineer

• Design, develop, and run tooling to discover problems in data and applications and report the issues to engineering and product leadership
• Review and analyze complex software enhancement initiatives for business, operational or technical improvements that require in depth evaluation of multiple factors including intangibles or unprecedented factors
• Make decisions in complex and multi-faceted data engineering situations requiring understanding of software package options and programming language and compliance requirements that influence and lead Technology to meet deliverables and drive organizational change
• Create standardized feature transformation pipelines and a common feature store with strong lineage, dictionary and high availability for models
• Implement robust data modeling (dimensional, data vault, or canonical models)
• Enforce data quality, lineage, and observability using standardized metrics, validation rules, and monitoring dashboards.
• Document patterns, runbooks, and best practices, and provide enablement through workshops and code examples

Lead Data Engineer

• Design, build, and maintain robust data pipelines and data lake architecture for both batch and real-time streaming use cases, including high-volume, low-latency data processing
• Improve observability, alerting, and SLOs across data systems so pipelines are easier to monitor and issues are caught early
• Optimize ETL/ELT workflows for performance, scalability, and fault tolerance
• Develop dbt workflows to onboard Evaluation Partners and create end-of-day reporting for partner analysis
• Build and support event-driven architectures and scalable platform components
• Contribute to the orchestration and automation of workflows
• Integrate complex financial APIs and third-party data sources into internal systems
• Collaborate with analytics, product, and ML engineers to develop and deploy reliable data products
• Work on feature pipelines and model-ready data to support ML engineers
• Promote high standards in code quality, testing, and platform reliability
• Participate in Agile ceremonies and foster a collaborative, growth-oriented team culture

• Solving our unique data-lake challenges: transforming and seamlessly normalizing highly varied partner datasets (such as donation data);
• Design robust batch-processing pipelines capable of extracting and loading massive datasets from a variety of internal, external, and public sources;
• Developing processes for data mining, data modeling, and data production;
• Leveraging modern technology knowledge to champion evolving industry trends, updated design patterns, and engineering excellence across the team;
• Autonomously scope, design, and execute complex data projects from day one, turning ambiguous requirements into clear, decisive technical plans;
• Developing robust testing and monitoring systems for scheduled processes;
• Collaborating with cross-functional teams to support their data infrastructure needs;
• Joining each and every one of your colleagues in creating an inclusive workspace.

• Lead the architecture and development of scalable, secure data pipelines supporting AI/ML workloads
• Own end to end data engineering processes: ingestion, transformation, storage, quality, and monitoring
• Collaborate with data scientists and ML engineers on model features, training pipelines, and deployment
• Drive best practices in data modeling, orchestration, versioning, and performance optimization
• Mentor and guide junior engineers
• Ensure data governance, lineage, and compliance standards are met across platforms
• Support real time and batch processing frameworks in production environments

• Lead a distributed engineering team across platform/product engineering, connectors, QA, DevOps/infra, AI implementation, data governance, and support.
• Own delivery, quality, release discipline, and execution of the technical roadmap.
• Install engineering discipline where it's thin: automated testing, QA, CI/CD, release governance, and versioning.
• Drive repeatable, perimeter-safe deployments, including containerization, infrastructure-as-code, secure deployment, and SOC 2 readiness.
• Partner with the Chief Architect on the connector framework, canonical metadata model, architecture decisions, and product IP.
• Build the team: assess current talent, retain the strong, hire the gaps, and align the team plan to the roadmap.
• Push practical AI use across coding, review, testing, ops, and engineering productivity.

Director of Engineering – Data Platform

• Define and drive enterprise data architecture strategy
• Establish target-state data platform architectures and standards aligned to business priorities.
• Translate business requirements into scalable data architecture solutions.
• Design and govern enterprise data ecosystems.
• Architect data solutions and integration strategies across systems.
• Ensure consistent access, integration, and consumption of data across platforms.
• Lead data architecture governance and quality initiatives.
• Define standards for data quality, metadata, master data, and lifecycle management.
• Establish data governance policies, compliance controls, and security practices.
• Drive platform and technology decisions.
• Evaluate and recommend data platforms, tools, and emerging technologies.
• Define standards for data integration, pipelines, and data services.
• Collaborate across business, data, and technology teams.
• Partner with stakeholders to align data architecture with enterprise objectives.
• Act as a bridge between data strategy and technical execution.
• Provide leadership and oversight.
• Lead architecture reviews and ensure adherence to standards.
• Support delivery teams through the product lifecycle.

Enterprise Data Architect

• Oversee the strategic product management for an assigned technical product line(s)
• Engage with key stakeholders to define, document, and prioritize features
• Ensure delivery of marketable capabilities for U.S. Bank engineering teams
• Drive product development with ownership of backlog
• Develop, analyze, monitor, and report on performance metrics

Technical Product Manager – Data Platform, MDM, Observability, Marketplace

• Define and drive enterprise data architecture strategy
• Establish target-state data platform architectures and standards aligned to business priorities.
• Translate business requirements into scalable data architecture solutions.
• Design and govern enterprise data ecosystems
• Architect data solutions and integration strategies across systems.
• Ensure consistent access, integration, and consumption of data across platforms.
• Lead data architecture governance and quality initiatives
• Define standards for data quality, metadata, master data, and lifecycle management.
• Establish data governance policies, compliance controls, and security practices.
• Drive platform and technology decisions
• Evaluate and recommend data platforms, tools, and emerging technologies.
• Define standards for data integration, pipelines, and data services.
• Collaborate across business, data, and technology teams
• Partner with stakeholders to align data architecture with enterprise objectives.
• Act as a bridge between data strategy and technical execution.
• Provide leadership and oversight
• Lead architecture reviews and ensure adherence to standards
• Support delivery teams through the product lifecycle.

• Design, build, and operate batch and streaming data pipelines on modern cloud data platforms.
• Develop robust ETL/ELT processes using SQL, Python, and PySpark.
• Implement layered / medallion data architectures and analytics-ready data models.
• Lead the implementation and evolution of data governance practices.
• Define and operationalize governance standards for data products and AI/ML features.
• Design and build AI agents and intelligent automation workflows.

Senior Data Engineer, Governance Focus

• Design, implement, and maintain logical and physical data structures within the MIR Data Layer
• Build and evolve metadata-driven schemas supporting: Document metadata, Transaction and workflow state, Event based and audit data, Reporting and analytics use cases
• Implement data solutions that support large-scale, concurrent workloads
• Optimize schemas, indexes, and access patterns to improve throughput and latency
• Leverage Memory-Optimized Tables (In-Memory OLTP) for performance-critical paths where appropriate, under architectural guidance
• Implement and support event-driven data processing within the MIR Data Layer
• Work with SQL Service Broker queues and services, including: Asynchronous processing, Status tracking and reconciliation, Retry and error-handling patterns
• Help ensure event processing is idempotent, traceable, and operationally reliable
• Build data structures that enable enterprise reporting and analytics without impacting transactional workloads
• Support snapshotting and reporting-friendly schemas for downstream consumers
• Assist in ensuring audit data is accurate, consistent, and compliant with regulatory expectations
• Contribute to cloud-readiness efforts for the MIR Data Layer
• Support migration and proof-of-concept work targeting: Amazon RDS for SQL Server, Azure SQL Database / Azure SQL Managed Instance
• Help adapt data designs to managed-platform constraints while preserving performance and correctness
• Participate in production readiness, stability, and incident resolution for the MIR Data Layer
• Debug and resolve complex production issues, including: Performance degradation, Blocking, deadlocks, and resource contention, Event based or Service Broker processing failures
• Partner closely with DBAs, SRE, and platform teams to improve monitoring and alerting, Support HA/DR and failover scenarios, Reduce repeat data-layer incidents
• Participate in on-call rotations or escalation coverage as appropriate for a senior-level role
• Ensure new data-layer implementations meet operability and supportability standards before release

• Build and support scalable data engineering solutions using Azure Databricks, PySpark, SQL, Delta Lake, Azure Data Factory, dbt, and Unity Catalog.
• Improve metadata-driven Azure Data Factory and Databricks patterns for orchestration, configuration, monitoring, restartability, and operational support.
• Develop reusable accelerators including CI/CD templates, Databricks Asset Bundle patterns, deployment automation, environment configuration, and data product onboarding templates.
• Design, develop, and support dbt models, macros, tests, documentation, and transformation standards for governed analytical data products.
• Provide guidance on appropriate technology selection and implementation patterns across dbt, Databricks notebooks and workflows, Delta Live Tables, Spark, and Starburst/Trino.
• Support cross-domain analytics initiatives by transforming source-refined data into trusted, reusable, business-aligned derivative data products.
• Leverage Unity Catalog to establish and support governed catalogs, schemas, tables, lineage, access controls, naming standards, and certification practices.
• Support Starburst/Trino as an analytical and federated query layer for governed enterprise data consumption.
• Apply Azure DevOps, Git, CI/CD, and Infrastructure as Code (IaC) practices to create repeatable, testable, and environment-aware platform delivery processes.
• Troubleshoot and resolve production issues related to orchestration, transformations, data quality, access management, query performance, deployments, and operational workflows.
• Collaborate with data engineering, analytics, platform, governance, and business teams to establish reusable, scalable, and supportable data engineering patterns.
• Contribute to the evolution of enterprise data engineering standards, governance practices, observability capabilities, and AI-ready data product frameworks.

Data Engineer jobs in Minnesota

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Curriculum Development Manager, Data Platforms

Advanced Specialist, Data Engineering

Data Architect

Senior Software Engineer, Data

Senior Software Engineer, ERP, Data & Domain

Senior Data Engineer

Lead Director, Platform Product Management – Data Platform & Operations

Engineering Development Program – Data Engineering

Staff Data Engineer

Lead Data Engineer

Senior Data Engineer

Staff Data Engineer

Lead Data Engineer

Director of Engineering – Data Platform

Enterprise Data Architect

Technical Product Manager – Data Platform, MDM, Observability, Marketplace

Enterprise Data Architect

Senior Data Engineer, Governance Focus

Senior Data Engineer

Senior Artificial Intelligence Data Engineer